Overview

Brought to you by YData

Dataset statistics

Number of variables 21
Number of observations 50000
Missing cells 0
Missing cells (%) 0.0%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 32.7 MiB
Average record size in memory 685.2 B

Variable types

Text 2
Numeric 7
Categorical 11
DateTime 1

Alerts

Failed_Transaction_Count_7d is highly overall correlated with Fraud_Label High correlation
Fraud_Label is highly overall correlated with Failed_Transaction_Count_7d and 1 other fields High correlation
Risk_Score is highly overall correlated with Fraud_Label High correlation
IP_Address_Flag is highly imbalanced (71.3%) Imbalance
Previous_Fraudulent_Activity is highly imbalanced (53.6%) Imbalance
Transaction_ID has unique values Unique

Reproduction

Analysis started 2025-04-07 17:15:24.046388
Analysis finished 2025-04-07 17:15:50.560892
Duration 26.51 seconds
Software version ydata-profiling vv4.16.1
Download configuration config.json

Variables

Transaction_ID
Text

Unique 

Distinct 50000
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Memory size 3.1 MiB
2025-04-07T17:15:51.322303 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 9
Median length 9
Mean length 8.7778
Min length 5

Characters and Unicode

Total characters 438890
Distinct characters 14
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 50000 ?
Unique (%) 100.0%

Sample

1st row TXN_33553
2nd row TXN_9427
3rd row TXN_199
4th row TXN_12447
5th row TXN_39489
Value Count Frequency (%)
txn_2433 1
 
< 0.1%
txn_769 1
 
< 0.1%
txn_1685 1
 
< 0.1%
txn_41090 1
 
< 0.1%
txn_16023 1
 
< 0.1%
txn_44131 1
 
< 0.1%
txn_47191 1
 
< 0.1%
txn_21962 1
 
< 0.1%
txn_37194 1
 
< 0.1%
txn_16850 1
 
< 0.1%
Other values (49990) 49990
> 99.9%
2025-04-07T17:15:51.993698 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
T 50000
11.4%
X 50000
11.4%
N 50000
11.4%
_ 50000
11.4%
3 30000
 
6.8%
1 30000
 
6.8%
4 30000
 
6.8%
2 30000
 
6.8%
5 20000
 
4.6%
7 20000
 
4.6%
Other values (4) 78890
18.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 438890
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
T 50000
11.4%
X 50000
11.4%
N 50000
11.4%
_ 50000
11.4%
3 30000
 
6.8%
1 30000
 
6.8%
4 30000
 
6.8%
2 30000
 
6.8%
5 20000
 
4.6%
7 20000
 
4.6%
Other values (4) 78890
18.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 438890
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
T 50000
11.4%
X 50000
11.4%
N 50000
11.4%
_ 50000
11.4%
3 30000
 
6.8%
1 30000
 
6.8%
4 30000
 
6.8%
2 30000
 
6.8%
5 20000
 
4.6%
7 20000
 
4.6%
Other values (4) 78890
18.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 438890
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
T 50000
11.4%
X 50000
11.4%
N 50000
11.4%
_ 50000
11.4%
3 30000
 
6.8%
1 30000
 
6.8%
4 30000
 
6.8%
2 30000
 
6.8%
5 20000
 
4.6%
7 20000
 
4.6%
Other values (4) 78890
18.0%

User_ID
Text

Distinct 8963
Distinct (%) 17.9%
Missing 0
Missing (%) 0.0%
Memory size 3.1 MiB
2025-04-07T17:15:52.367779 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 9
Median length 9
Mean length 9
Min length 9

Characters and Unicode

Total characters 450000
Distinct characters 15
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 195 ?
Unique (%) 0.4%

Sample

1st row USER_1834
2nd row USER_7875
3rd row USER_2734
4th row USER_2617
5th row USER_2014
Value Count Frequency (%)
user_3925 16
 
< 0.1%
user_6599 16
 
< 0.1%
user_9998 16
 
< 0.1%
user_3415 15
 
< 0.1%
user_1027 15
 
< 0.1%
user_5014 15
 
< 0.1%
user_8008 14
 
< 0.1%
user_9995 14
 
< 0.1%
user_6243 14
 
< 0.1%
user_6906 14
 
< 0.1%
Other values (8953) 49851
99.7%
2025-04-07T17:15:52.803658 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
U 50000
11.1%
S 50000
11.1%
E 50000
11.1%
R 50000
11.1%
_ 50000
11.1%
6 20755
 
4.6%
7 20665
 
4.6%
1 20588
 
4.6%
3 20521
 
4.6%
4 20505
 
4.6%
Other values (5) 96966
21.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 450000
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
U 50000
11.1%
S 50000
11.1%
E 50000
11.1%
R 50000
11.1%
_ 50000
11.1%
6 20755
 
4.6%
7 20665
 
4.6%
1 20588
 
4.6%
3 20521
 
4.6%
4 20505
 
4.6%
Other values (5) 96966
21.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 450000
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
U 50000
11.1%
S 50000
11.1%
E 50000
11.1%
R 50000
11.1%
_ 50000
11.1%
6 20755
 
4.6%
7 20665
 
4.6%
1 20588
 
4.6%
3 20521
 
4.6%
4 20505
 
4.6%
Other values (5) 96966
21.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 450000
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
U 50000
11.1%
S 50000
11.1%
E 50000
11.1%
R 50000
11.1%
_ 50000
11.1%
6 20755
 
4.6%
7 20665
 
4.6%
1 20588
 
4.6%
3 20521
 
4.6%
4 20505
 
4.6%
Other values (5) 96966
21.5%

Transaction_Amount
Real number (ℝ)

Distinct 21763
Distinct (%) 43.5%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 99.411012
Minimum 0
Maximum 1174.14
Zeros 2
Zeros (%) < 0.1%
Negative 0
Negative (%) 0.0%
Memory size 390.8 KiB
2025-04-07T17:15:52.935062 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 5.1
Q1 28.6775
median 69.66
Q3 138.8525
95-th percentile 294.3035
Maximum 1174.14
Range 1174.14
Interquartile range (IQR) 110.175

Descriptive statistics

Standard deviation 98.687292
Coefficient of variation (CV) 0.99271992
Kurtosis 6.1293396
Mean 99.411012
Median Absolute Deviation (MAD) 48.56
Skewness 1.9960359
Sum 4970550.6
Variance 9739.1816
Monotonicity Not monotonic
2025-04-07T17:15:53.064224 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
7.65 14
 
< 0.1%
8.74 13
 
< 0.1%
0.24 13
 
< 0.1%
18.58 12
 
< 0.1%
6.5 12
 
< 0.1%
13.75 12
 
< 0.1%
25.63 11
 
< 0.1%
0.91 11
 
< 0.1%
3.64 11
 
< 0.1%
5.11 11
 
< 0.1%
Other values (21753) 49880
99.8%
Value Count Frequency (%)
0 2
 
< 0.1%
0.01 5
< 0.1%
0.02 4
< 0.1%
0.03 5
< 0.1%
0.04 5
< 0.1%
0.05 6
< 0.1%
0.06 6
< 0.1%
0.07 6
< 0.1%
0.08 2
 
< 0.1%
0.09 5
< 0.1%
Value Count Frequency (%)
1174.14 1
< 0.1%
1005.32 1
< 0.1%
971.61 1
< 0.1%
898.8 1
< 0.1%
892.67 1
< 0.1%
886.3 1
< 0.1%
875.71 1
< 0.1%
874.85 1
< 0.1%
868.75 1
< 0.1%
864.36 1
< 0.1%

Transaction_Type
Categorical

Distinct 4
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 3.1 MiB
POS
12549 
Online
12546 
ATM Withdrawal
12453 
Bank Transfer
12452 

Length

Max length 14
Median length 13
Mean length 8.98282
Min length 3

Characters and Unicode

Total characters 449141
Distinct characters 22
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row POS
2nd row Bank Transfer
3rd row Online
4th row ATM Withdrawal
5th row POS

Common Values

Value Count Frequency (%)
POS 12549
25.1%
Online 12546
25.1%
ATM Withdrawal 12453
24.9%
Bank Transfer 12452
24.9%

Length

2025-04-07T17:15:53.201490 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:53.280891 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
pos 12549
16.8%
online 12546
16.7%
atm 12453
16.6%
withdrawal 12453
16.6%
bank 12452
16.6%
transfer 12452
16.6%

Most occurring characters

Value Count Frequency (%)
n 49996
 
11.1%
a 49810
 
11.1%
r 37357
 
8.3%
O 25095
 
5.6%
i 24999
 
5.6%
l 24999
 
5.6%
e 24998
 
5.6%
T 24905
 
5.5%
24905
 
5.5%
P 12549
 
2.8%
Other values (12) 149528
33.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 449141
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
n 49996
 
11.1%
a 49810
 
11.1%
r 37357
 
8.3%
O 25095
 
5.6%
i 24999
 
5.6%
l 24999
 
5.6%
e 24998
 
5.6%
T 24905
 
5.5%
24905
 
5.5%
P 12549
 
2.8%
Other values (12) 149528
33.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 449141
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
n 49996
 
11.1%
a 49810
 
11.1%
r 37357
 
8.3%
O 25095
 
5.6%
i 24999
 
5.6%
l 24999
 
5.6%
e 24998
 
5.6%
T 24905
 
5.5%
24905
 
5.5%
P 12549
 
2.8%
Other values (12) 149528
33.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 449141
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
n 49996
 
11.1%
a 49810
 
11.1%
r 37357
 
8.3%
O 25095
 
5.6%
i 24999
 
5.6%
l 24999
 
5.6%
e 24998
 
5.6%
T 24905
 
5.5%
24905
 
5.5%
P 12549
 
2.8%
Other values (12) 149528
33.3%

Timestamp
Date

Distinct 47724
Distinct (%) 95.4%
Missing 0
Missing (%) 0.0%
Memory size 390.8 KiB
Minimum 2023-01-01 00:01:00
Maximum 2023-12-31 23:50:00
Invalid dates 0
Invalid dates (%) 0.0%
2025-04-07T17:15:53.427447 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:53.569188 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Account_Balance
Real number (ℝ)

Distinct 49867
Distinct (%) 99.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 50294.066
Minimum 500.48
Maximum 99998.31
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 390.8 KiB
2025-04-07T17:15:53.709974 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 500.48
5-th percentile 5498.417
Q1 25355.995
median 50384.43
Q3 75115.135
95-th percentile 95118.842
Maximum 99998.31
Range 99497.83
Interquartile range (IQR) 49759.14

Descriptive statistics

Standard deviation 28760.459
Coefficient of variation (CV) 0.57184596
Kurtosis -1.2013596
Mean 50294.066
Median Absolute Deviation (MAD) 24869.18
Skewness -0.0030254836
Sum 2.5147033 × 109
Variance 8.2716398 × 108
Monotonicity Not monotonic
2025-04-07T17:15:53.856593 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
94424.84 2
 
< 0.1%
99981.36 2
 
< 0.1%
70746.35 2
 
< 0.1%
87442.63 2
 
< 0.1%
81203.56 2
 
< 0.1%
17904.99 2
 
< 0.1%
11689.27 2
 
< 0.1%
27506.1 2
 
< 0.1%
14169.82 2
 
< 0.1%
76119.68 2
 
< 0.1%
Other values (49857) 49980
> 99.9%
Value Count Frequency (%)
500.48 1
< 0.1%
503.44 1
< 0.1%
503.94 1
< 0.1%
504.84 1
< 0.1%
509.9 1
< 0.1%
511.68 1
< 0.1%
514.57 1
< 0.1%
514.61 1
< 0.1%
515.78 1
< 0.1%
519.91 1
< 0.1%
Value Count Frequency (%)
99998.31 1
< 0.1%
99997.94 1
< 0.1%
99997.79 1
< 0.1%
99997.52 1
< 0.1%
99991.35 1
< 0.1%
99991.04 1
< 0.1%
99988.27 2
< 0.1%
99985.5 1
< 0.1%
99984.15 1
< 0.1%
99982.34 1
< 0.1%

Device_Type
Categorical

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 3.0 MiB
Tablet
16779 
Mobile
16640 
Laptop
16581 

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 300000
Distinct characters 11
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Laptop
2nd row Mobile
3rd row Tablet
4th row Tablet
5th row Mobile

Common Values

Value Count Frequency (%)
Tablet 16779
33.6%
Mobile 16640
33.3%
Laptop 16581
33.2%

Length

2025-04-07T17:15:53.972227 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.044651 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
tablet 16779
33.6%
mobile 16640
33.3%
laptop 16581
33.2%

Most occurring characters

Value Count Frequency (%)
b 33419
11.1%
e 33419
11.1%
l 33419
11.1%
t 33360
11.1%
a 33360
11.1%
o 33221
11.1%
p 33162
11.1%
T 16779
5.6%
M 16640
5.5%
i 16640
5.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
b 33419
11.1%
e 33419
11.1%
l 33419
11.1%
t 33360
11.1%
a 33360
11.1%
o 33221
11.1%
p 33162
11.1%
T 16779
5.6%
M 16640
5.5%
i 16640
5.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
b 33419
11.1%
e 33419
11.1%
l 33419
11.1%
t 33360
11.1%
a 33360
11.1%
o 33221
11.1%
p 33162
11.1%
T 16779
5.6%
M 16640
5.5%
i 16640
5.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
b 33419
11.1%
e 33419
11.1%
l 33419
11.1%
t 33360
11.1%
a 33360
11.1%
o 33221
11.1%
p 33162
11.1%
T 16779
5.6%
M 16640
5.5%
i 16640
5.5%

Location
Categorical

Distinct 5
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 3.0 MiB
Tokyo
10208 
Mumbai
9994 
London
9945 
Sydney
9938 
New York
9915 

Length

Max length 8
Median length 6
Mean length 6.19244
Min length 5

Characters and Unicode

Total characters 309622
Distinct characters 20
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Sydney
2nd row New York
3rd row Mumbai
4th row New York
5th row Mumbai

Common Values

Value Count Frequency (%)
Tokyo 10208
20.4%
Mumbai 9994
20.0%
London 9945
19.9%
Sydney 9938
19.9%
New York 9915
19.8%

Length

2025-04-07T17:15:54.148396 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.231186 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
tokyo 10208
17.0%
mumbai 9994
16.7%
london 9945
16.6%
sydney 9938
16.6%
new 9915
16.5%
york 9915
16.5%

Most occurring characters

Value Count Frequency (%)
o 50221
16.2%
y 30084
 
9.7%
n 29828
 
9.6%
k 20123
 
6.5%
d 19883
 
6.4%
e 19853
 
6.4%
T 10208
 
3.3%
M 9994
 
3.2%
b 9994
 
3.2%
a 9994
 
3.2%
Other values (10) 99440
32.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 309622
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
o 50221
16.2%
y 30084
 
9.7%
n 29828
 
9.6%
k 20123
 
6.5%
d 19883
 
6.4%
e 19853
 
6.4%
T 10208
 
3.3%
M 9994
 
3.2%
b 9994
 
3.2%
a 9994
 
3.2%
Other values (10) 99440
32.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 309622
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
o 50221
16.2%
y 30084
 
9.7%
n 29828
 
9.6%
k 20123
 
6.5%
d 19883
 
6.4%
e 19853
 
6.4%
T 10208
 
3.3%
M 9994
 
3.2%
b 9994
 
3.2%
a 9994
 
3.2%
Other values (10) 99440
32.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 309622
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
o 50221
16.2%
y 30084
 
9.7%
n 29828
 
9.6%
k 20123
 
6.5%
d 19883
 
6.4%
e 19853
 
6.4%
T 10208
 
3.3%
M 9994
 
3.2%
b 9994
 
3.2%
a 9994
 
3.2%
Other values (10) 99440
32.1%

Merchant_Category
Categorical

Distinct 5
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 3.1 MiB
Clothing
10033 
Groceries
10019 
Travel
10015 
Restaurants
9976 
Electronics
9957 

Length

Max length 11
Median length 9
Mean length 8.99576
Min length 6

Characters and Unicode

Total characters 449788
Distinct characters 19
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Travel
2nd row Clothing
3rd row Restaurants
4th row Clothing
5th row Electronics

Common Values

Value Count Frequency (%)
Clothing 10033
20.1%
Groceries 10019
20.0%
Travel 10015
20.0%
Restaurants 9976
20.0%
Electronics 9957
19.9%

Length

2025-04-07T17:15:54.342485 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.427802 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
clothing 10033
20.1%
groceries 10019
20.0%
travel 10015
20.0%
restaurants 9976
20.0%
electronics 9957
19.9%

Most occurring characters

Value Count Frequency (%)
r 49986
11.1%
e 49986
11.1%
t 39942
8.9%
s 39928
8.9%
i 30009
 
6.7%
o 30009
 
6.7%
l 30005
 
6.7%
a 29967
 
6.7%
n 29966
 
6.7%
c 29933
 
6.7%
Other values (9) 90057
20.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 449788
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
r 49986
11.1%
e 49986
11.1%
t 39942
8.9%
s 39928
8.9%
i 30009
 
6.7%
o 30009
 
6.7%
l 30005
 
6.7%
a 29967
 
6.7%
n 29966
 
6.7%
c 29933
 
6.7%
Other values (9) 90057
20.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 449788
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
r 49986
11.1%
e 49986
11.1%
t 39942
8.9%
s 39928
8.9%
i 30009
 
6.7%
o 30009
 
6.7%
l 30005
 
6.7%
a 29967
 
6.7%
n 29966
 
6.7%
c 29933
 
6.7%
Other values (9) 90057
20.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 449788
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
r 49986
11.1%
e 49986
11.1%
t 39942
8.9%
s 39928
8.9%
i 30009
 
6.7%
o 30009
 
6.7%
l 30005
 
6.7%
a 29967
 
6.7%
n 29966
 
6.7%
c 29933
 
6.7%
Other values (9) 90057
20.0%

IP_Address_Flag
Categorical

Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 2.8 MiB
0
47490 
1
 
2510

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 50000
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 0
4th row 0
5th row 0

Common Values

Value Count Frequency (%)
0 47490
95.0%
1 2510
 
5.0%

Length

2025-04-07T17:15:54.531917 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.610106 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 47490
95.0%
1 2510
 
5.0%

Most occurring characters

Value Count Frequency (%)
0 47490
95.0%
1 2510
 
5.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 47490
95.0%
1 2510
 
5.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 47490
95.0%
1 2510
 
5.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 47490
95.0%
1 2510
 
5.0%

Previous_Fraudulent_Activity
Categorical

Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 2.8 MiB
0
45080 
1
4920 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 50000
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 0
4th row 0
5th row 1

Common Values

Value Count Frequency (%)
0 45080
90.2%
1 4920
 
9.8%

Length

2025-04-07T17:15:54.688425 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:54.755696 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 45080
90.2%
1 4920
 
9.8%

Most occurring characters

Value Count Frequency (%)
0 45080
90.2%
1 4920
 
9.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 45080
90.2%
1 4920
 
9.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 45080
90.2%
1 4920
 
9.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 45080
90.2%
1 4920
 
9.8%

Daily_Transaction_Count
Real number (ℝ)

Distinct 14
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 7.48524
Minimum 1
Maximum 14
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 390.8 KiB
2025-04-07T17:15:54.826941 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 4
median 7
Q3 11
95-th percentile 14
Maximum 14
Range 13
Interquartile range (IQR) 7

Descriptive statistics

Standard deviation 4.0396372
Coefficient of variation (CV) 0.53968038
Kurtosis -1.2212633
Mean 7.48524
Median Absolute Deviation (MAD) 4
Skewness 0.0036841391
Sum 374262
Variance 16.318669
Monotonicity Not monotonic
2025-04-07T17:15:54.930926 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
Value Count Frequency (%)
3 3634
 
7.3%
10 3623
 
7.2%
11 3620
 
7.2%
4 3606
 
7.2%
2 3605
 
7.2%
1 3598
 
7.2%
12 3586
 
7.2%
5 3582
 
7.2%
7 3574
 
7.1%
14 3571
 
7.1%
Other values (4) 14001
28.0%
Value Count Frequency (%)
1 3598
7.2%
2 3605
7.2%
3 3634
7.3%
4 3606
7.2%
5 3582
7.2%
6 3521
7.0%
7 3574
7.1%
8 3418
6.8%
9 3538
7.1%
10 3623
7.2%
Value Count Frequency (%)
14 3571
7.1%
13 3524
7.0%
12 3586
7.2%
11 3620
7.2%
10 3623
7.2%
9 3538
7.1%
8 3418
6.8%
7 3574
7.1%
6 3521
7.0%
5 3582
7.2%

Avg_Transaction_Amount_7d
Real number (ℝ)

Distinct 31420
Distinct (%) 62.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 255.27192
Minimum 10
Maximum 500
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 390.8 KiB
2025-04-07T17:15:55.074017 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 10
5-th percentile 34.84
Q1 132.0875
median 256.085
Q3 378.0325
95-th percentile 475.611
Maximum 500
Range 490
Interquartile range (IQR) 245.945

Descriptive statistics

Standard deviation 141.38228
Coefficient of variation (CV) 0.5538497
Kurtosis -1.1991936
Mean 255.27192
Median Absolute Deviation (MAD) 122.875
Skewness -0.00090920079
Sum 12763596
Variance 19988.949
Monotonicity Not monotonic
2025-04-07T17:15:55.218588 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
384.72 7
 
< 0.1%
82.26 7
 
< 0.1%
226.4 7
 
< 0.1%
454.19 6
 
< 0.1%
422.33 6
 
< 0.1%
288.61 6
 
< 0.1%
450.79 6
 
< 0.1%
39.52 6
 
< 0.1%
245.36 6
 
< 0.1%
112.26 6
 
< 0.1%
Other values (31410) 49937
99.9%
Value Count Frequency (%)
10 1
 
< 0.1%
10.01 2
< 0.1%
10.03 2
< 0.1%
10.05 1
 
< 0.1%
10.06 3
< 0.1%
10.07 1
 
< 0.1%
10.08 1
 
< 0.1%
10.09 1
 
< 0.1%
10.1 1
 
< 0.1%
10.11 1
 
< 0.1%
Value Count Frequency (%)
500 1
< 0.1%
499.99 2
< 0.1%
499.98 2
< 0.1%
499.96 1
< 0.1%
499.95 1
< 0.1%
499.94 1
< 0.1%
499.93 1
< 0.1%
499.91 2
< 0.1%
499.89 1
< 0.1%
499.87 2
< 0.1%

Failed_Transaction_Count_7d
Categorical

High correlation 

Distinct 5
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 2.8 MiB
3
10216 
0
10014 
4
9954 
1
9919 
2
9897 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 50000
Distinct characters 5
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 3
2nd row 4
3rd row 4
4th row 4
5th row 4

Common Values

Value Count Frequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Length

2025-04-07T17:15:55.342157 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:55.432949 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Most occurring characters

Value Count Frequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
3 10216
20.4%
0 10014
20.0%
4 9954
19.9%
1 9919
19.8%
2 9897
19.8%

Card_Type
Categorical

Distinct 4
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 3.0 MiB
Mastercard
12693 
Visa
12560 
Amex
12419 
Discover
12328 

Length

Max length 10
Median length 8
Mean length 6.5094
Min length 4

Characters and Unicode

Total characters 325470
Distinct characters 16
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Amex
2nd row Mastercard
3rd row Visa
4th row Visa
5th row Mastercard

Common Values

Value Count Frequency (%)
Mastercard 12693
25.4%
Visa 12560
25.1%
Amex 12419
24.8%
Discover 12328
24.7%

Length

2025-04-07T17:15:55.545592 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:55.645112 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
mastercard 12693
25.4%
visa 12560
25.1%
amex 12419
24.8%
discover 12328
24.7%

Most occurring characters

Value Count Frequency (%)
a 37946
11.7%
r 37714
11.6%
s 37581
11.5%
e 37440
11.5%
c 25021
 
7.7%
i 24888
 
7.6%
t 12693
 
3.9%
M 12693
 
3.9%
d 12693
 
3.9%
V 12560
 
3.9%
Other values (6) 74241
22.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 325470
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
a 37946
11.7%
r 37714
11.6%
s 37581
11.5%
e 37440
11.5%
c 25021
 
7.7%
i 24888
 
7.6%
t 12693
 
3.9%
M 12693
 
3.9%
d 12693
 
3.9%
V 12560
 
3.9%
Other values (6) 74241
22.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 325470
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
a 37946
11.7%
r 37714
11.6%
s 37581
11.5%
e 37440
11.5%
c 25021
 
7.7%
i 24888
 
7.6%
t 12693
 
3.9%
M 12693
 
3.9%
d 12693
 
3.9%
V 12560
 
3.9%
Other values (6) 74241
22.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 325470
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
a 37946
11.7%
r 37714
11.6%
s 37581
11.5%
e 37440
11.5%
c 25021
 
7.7%
i 24888
 
7.6%
t 12693
 
3.9%
M 12693
 
3.9%
d 12693
 
3.9%
V 12560
 
3.9%
Other values (6) 74241
22.8%

Card_Age
Real number (ℝ)

Distinct 239
Distinct (%) 0.5%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 119.99994
Minimum 1
Maximum 239
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 390.8 KiB
2025-04-07T17:15:55.764888 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 13
Q1 60
median 120
Q3 180
95-th percentile 228
Maximum 239
Range 238
Interquartile range (IQR) 120

Descriptive statistics

Standard deviation 68.985817
Coefficient of variation (CV) 0.5748821
Kurtosis -1.2019273
Mean 119.99994
Median Absolute Deviation (MAD) 60
Skewness 0.00051173999
Sum 5999997
Variance 4759.043
Monotonicity Not monotonic
2025-04-07T17:15:55.911443 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
80 247
 
0.5%
31 244
 
0.5%
84 243
 
0.5%
63 239
 
0.5%
55 239
 
0.5%
218 236
 
0.5%
29 235
 
0.5%
73 235
 
0.5%
168 234
 
0.5%
75 234
 
0.5%
Other values (229) 47614
95.2%
Value Count Frequency (%)
1 227
0.5%
2 213
0.4%
3 226
0.5%
4 209
0.4%
5 201
0.4%
6 191
0.4%
7 205
0.4%
8 213
0.4%
9 180
0.4%
10 187
0.4%
Value Count Frequency (%)
239 215
0.4%
238 216
0.4%
237 184
0.4%
236 187
0.4%
235 209
0.4%
234 218
0.4%
233 230
0.5%
232 211
0.4%
231 202
0.4%
230 207
0.4%

Transaction_Distance
Real number (ℝ)

Distinct 47546
Distinct (%) 95.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 2499.1642
Minimum 0.25
Maximum 4999.93
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 390.8 KiB
2025-04-07T17:15:56.515888 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0.25
5-th percentile 253.4445
Q1 1256.4975
median 2490.785
Q3 3746.395
95-th percentile 4756.9255
Maximum 4999.93
Range 4999.68
Interquartile range (IQR) 2489.8975

Descriptive statistics

Standard deviation 1442.0138
Coefficient of variation (CV) 0.57699845
Kurtosis -1.1933281
Mean 2499.1642
Median Absolute Deviation (MAD) 1244.965
Skewness 0.0064491219
Sum 1.2495821 × 108
Variance 2079403.9
Monotonicity Not monotonic
2025-04-07T17:15:56.677600 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
951.92 4
 
< 0.1%
1743.63 4
 
< 0.1%
3447.51 3
 
< 0.1%
2264.45 3
 
< 0.1%
1199.61 3
 
< 0.1%
313.64 3
 
< 0.1%
122.87 3
 
< 0.1%
4913.77 3
 
< 0.1%
3035.56 3
 
< 0.1%
169.98 3
 
< 0.1%
Other values (47536) 49968
99.9%
Value Count Frequency (%)
0.25 1
< 0.1%
0.47 1
< 0.1%
0.56 1
< 0.1%
0.6 1
< 0.1%
0.66 1
< 0.1%
0.74 1
< 0.1%
1.1 1
< 0.1%
1.14 1
< 0.1%
1.46 1
< 0.1%
1.58 1
< 0.1%
Value Count Frequency (%)
4999.93 1
 
< 0.1%
4999.92 1
 
< 0.1%
4999.91 1
 
< 0.1%
4999.85 1
 
< 0.1%
4999.74 1
 
< 0.1%
4999.7 3
< 0.1%
4999.61 1
 
< 0.1%
4999.37 1
 
< 0.1%
4999.3 1
 
< 0.1%
4999.27 1
 
< 0.1%

Authentication_Method
Categorical

Distinct 4
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 3.0 MiB
Biometric
12591 
PIN
12586 
Password
12457 
OTP
12366 

Length

Max length 9
Median length 8
Mean length 5.75662
Min length 3

Characters and Unicode

Total characters 287831
Distinct characters 17
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Biometric
2nd row Password
3rd row Biometric
4th row OTP
5th row Password

Common Values

Value Count Frequency (%)
Biometric 12591
25.2%
PIN 12586
25.2%
Password 12457
24.9%
OTP 12366
24.7%

Length

2025-04-07T17:15:56.805122 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:56.883914 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
biometric 12591
25.2%
pin 12586
25.2%
password 12457
24.9%
otp 12366
24.7%

Most occurring characters

Value Count Frequency (%)
P 37409
13.0%
i 25182
 
8.7%
r 25048
 
8.7%
o 25048
 
8.7%
s 24914
 
8.7%
B 12591
 
4.4%
e 12591
 
4.4%
t 12591
 
4.4%
m 12591
 
4.4%
c 12591
 
4.4%
Other values (7) 87275
30.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 287831
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
P 37409
13.0%
i 25182
 
8.7%
r 25048
 
8.7%
o 25048
 
8.7%
s 24914
 
8.7%
B 12591
 
4.4%
e 12591
 
4.4%
t 12591
 
4.4%
m 12591
 
4.4%
c 12591
 
4.4%
Other values (7) 87275
30.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 287831
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
P 37409
13.0%
i 25182
 
8.7%
r 25048
 
8.7%
o 25048
 
8.7%
s 24914
 
8.7%
B 12591
 
4.4%
e 12591
 
4.4%
t 12591
 
4.4%
m 12591
 
4.4%
c 12591
 
4.4%
Other values (7) 87275
30.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 287831
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
P 37409
13.0%
i 25182
 
8.7%
r 25048
 
8.7%
o 25048
 
8.7%
s 24914
 
8.7%
B 12591
 
4.4%
e 12591
 
4.4%
t 12591
 
4.4%
m 12591
 
4.4%
c 12591
 
4.4%
Other values (7) 87275
30.3%

Risk_Score
Real number (ℝ)

High correlation 

Distinct 9931
Distinct (%) 19.9%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.50155552
Minimum 0.0001
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 390.8 KiB
2025-04-07T17:15:57.004908 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0.0001
5-th percentile 0.0518
Q1 0.254
median 0.50225
Q3 0.749525
95-th percentile 0.9505
Maximum 1
Range 0.9999
Interquartile range (IQR) 0.495525

Descriptive statistics

Standard deviation 0.28777412
Coefficient of variation (CV) 0.57376324
Kurtosis -1.1925911
Mean 0.50155552
Median Absolute Deviation (MAD) 0.24775
Skewness -0.0013190373
Sum 25077.776
Variance 0.082813944
Monotonicity Not monotonic
2025-04-07T17:15:57.167296 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0.6917 16
 
< 0.1%
0.3606 15
 
< 0.1%
0.299 15
 
< 0.1%
0.9781 14
 
< 0.1%
0.2228 14
 
< 0.1%
0.8594 14
 
< 0.1%
0.3546 14
 
< 0.1%
0.8236 14
 
< 0.1%
0.2526 14
 
< 0.1%
0.7457 13
 
< 0.1%
Other values (9921) 49857
99.7%
Value Count Frequency (%)
0.0001 6
< 0.1%
0.0002 6
< 0.1%
0.0003 4
< 0.1%
0.0004 3
< 0.1%
0.0005 6
< 0.1%
0.0006 3
< 0.1%
0.0007 2
 
< 0.1%
0.0008 6
< 0.1%
0.0009 7
< 0.1%
0.001 7
< 0.1%
Value Count Frequency (%)
1 1
 
< 0.1%
0.9999 6
< 0.1%
0.9998 3
< 0.1%
0.9997 6
< 0.1%
0.9996 5
< 0.1%
0.9995 3
< 0.1%
0.9994 2
 
< 0.1%
0.9993 2
 
< 0.1%
0.9992 1
 
< 0.1%
0.9991 4
< 0.1%

Is_Weekend
Categorical

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 2.8 MiB
0
35018 
1
14982 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 50000
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 0
4th row 0
5th row 1

Common Values

Value Count Frequency (%)
0 35018
70.0%
1 14982
30.0%

Length

2025-04-07T17:15:57.292487 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:57.365235 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 35018
70.0%
1 14982
30.0%

Most occurring characters

Value Count Frequency (%)
0 35018
70.0%
1 14982
30.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 35018
70.0%
1 14982
30.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 35018
70.0%
1 14982
30.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 35018
70.0%
1 14982
30.0%

Fraud_Label
Categorical

High correlation 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 2.8 MiB
0
33933 
1
16067 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 50000
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 1
3rd row 1
4th row 1
5th row 1

Common Values

Value Count Frequency (%)
0 33933
67.9%
1 16067
32.1%

Length

2025-04-07T17:15:57.453797 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T17:15:57.521056 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 33933
67.9%
1 16067
32.1%

Most occurring characters

Value Count Frequency (%)
0 33933
67.9%
1 16067
32.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 33933
67.9%
1 16067
32.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 33933
67.9%
1 16067
32.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 33933
67.9%
1 16067
32.1%

Interactions

2025-04-07T17:15:48.730816 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.307696 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.113722 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.952262 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.839909 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.001115 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.868684 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.847025 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.433770 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.239260 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.089365 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.964891 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.127939 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.001313 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.963094 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.548362 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.350989 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.211189 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.395864 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.256898 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.124975 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:49.086242 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.662021 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.473997 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.347047 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.515033 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.399721 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.255555 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:49.213087 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.773140 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.594047 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.473029 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.634727 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.517184 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.387850 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:49.332319 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.882905 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.713766 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.599548 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.752726 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.633606 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.500679 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:49.466642 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:43.997468 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:44.831331 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:45.719894 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:46.872721 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:47.748529 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-04-07T17:15:48.615978 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-07T17:15:57.604104 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Account_Balance Authentication_Method Avg_Transaction_Amount_7d Card_Age Card_Type Daily_Transaction_Count Device_Type Failed_Transaction_Count_7d Fraud_Label IP_Address_Flag Is_Weekend Location Merchant_Category Previous_Fraudulent_Activity Risk_Score Transaction_Amount Transaction_Distance Transaction_Type
Account_Balance 1.000 0.003 -0.002 0.001 0.007 0.006 0.000 0.000 0.005 0.000 0.007 0.009 0.000 0.010 -0.005 0.004 0.002 0.000
Authentication_Method 0.003 1.000 0.005 0.000 0.005 0.007 0.000 0.000 0.000 0.000 0.002 0.004 0.000 0.000 0.000 0.000 0.000 0.003
Avg_Transaction_Amount_7d -0.002 0.005 1.000 -0.009 0.000 0.005 0.009 0.006 0.000 0.000 0.002 0.006 0.004 0.000 0.005 -0.007 -0.003 0.000
Card_Age 0.001 0.000 -0.009 1.000 0.000 -0.001 0.011 0.003 0.000 0.000 0.000 0.000 0.004 0.000 -0.001 -0.003 -0.003 0.000
Card_Type 0.007 0.005 0.000 0.000 1.000 0.000 0.003 0.008 0.000 0.005 0.007 0.000 0.001 0.006 0.008 0.000 0.000 0.004
Daily_Transaction_Count 0.006 0.007 0.005 -0.001 0.000 1.000 0.000 0.004 0.000 0.001 0.009 0.000 0.001 0.006 -0.008 -0.002 -0.002 0.000
Device_Type 0.000 0.000 0.009 0.011 0.003 0.000 1.000 0.000 0.002 0.003 0.003 0.000 0.000 0.000 0.000 0.008 0.000 0.000
Failed_Transaction_Count_7d 0.000 0.000 0.006 0.003 0.008 0.004 0.000 1.000 0.725 0.006 0.000 0.004 0.000 0.000 0.000 0.006 0.000 0.003
Fraud_Label 0.005 0.000 0.000 0.000 0.000 0.000 0.002 0.725 1.000 0.000 0.000 0.000 0.000 0.000 0.552 0.000 0.004 0.000
IP_Address_Flag 0.000 0.000 0.000 0.000 0.005 0.001 0.003 0.006 0.000 1.000 0.002 0.012 0.000 0.007 0.000 0.009 0.000 0.000
Is_Weekend 0.007 0.002 0.002 0.000 0.007 0.009 0.003 0.000 0.000 0.002 1.000 0.008 0.000 0.000 0.000 0.000 0.009 0.000
Location 0.009 0.004 0.006 0.000 0.000 0.000 0.000 0.004 0.000 0.012 0.008 1.000 0.000 0.013 0.004 0.005 0.010 0.000
Merchant_Category 0.000 0.000 0.004 0.004 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.008 0.000 0.002
Previous_Fraudulent_Activity 0.010 0.000 0.000 0.000 0.006 0.006 0.000 0.000 0.000 0.007 0.000 0.013 0.000 1.000 0.000 0.000 0.000 0.000
Risk_Score -0.005 0.000 0.005 -0.001 0.008 -0.008 0.000 0.000 0.552 0.000 0.000 0.004 0.000 0.000 1.000 0.006 -0.002 0.000
Transaction_Amount 0.004 0.000 -0.007 -0.003 0.000 -0.002 0.008 0.006 0.000 0.009 0.000 0.005 0.008 0.000 0.006 1.000 0.003 0.000
Transaction_Distance 0.002 0.000 -0.003 -0.003 0.000 -0.002 0.000 0.000 0.004 0.000 0.009 0.010 0.000 0.000 -0.002 0.003 1.000 0.000
Transaction_Type 0.000 0.003 0.000 0.000 0.004 0.000 0.000 0.003 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.000 1.000

Missing values

2025-04-07T17:15:49.720928 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-07T17:15:50.136372 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Transaction_ID User_ID Transaction_Amount Transaction_Type Timestamp Account_Balance Device_Type Location Merchant_Category IP_Address_Flag Previous_Fraudulent_Activity Daily_Transaction_Count Avg_Transaction_Amount_7d Failed_Transaction_Count_7d Card_Type Card_Age Transaction_Distance Authentication_Method Risk_Score Is_Weekend Fraud_Label
0 TXN_33553 USER_1834 39.79 POS 14-08-2023 19:30 93213.17 Laptop Sydney Travel 0 0 7 437.63 3 Amex 65 883.17 Biometric 0.8494 0 0
1 TXN_9427 USER_7875 1.19 Bank Transfer 07-06-2023 04:01 75725.25 Mobile New York Clothing 0 0 13 478.76 4 Mastercard 186 2203.36 Password 0.0959 0 1
2 TXN_199 USER_2734 28.96 Online 20-06-2023 15:25 1588.96 Tablet Mumbai Restaurants 0 0 14 50.01 4 Visa 226 1909.29 Biometric 0.8400 0 1
3 TXN_12447 USER_2617 254.32 ATM Withdrawal 07-12-2023 00:31 76807.20 Tablet New York Clothing 0 0 8 182.48 4 Visa 76 1311.86 OTP 0.7935 0 1
4 TXN_39489 USER_2014 31.28 POS 11-11-2023 23:44 92354.66 Mobile Mumbai Electronics 0 1 14 328.69 4 Mastercard 140 966.98 Password 0.3819 1 1
5 TXN_42724 USER_6852 168.55 Online 05-06-2023 20:55 33236.94 Laptop Tokyo Restaurants 0 0 3 226.85 2 Discover 51 1725.64 OTP 0.0504 0 0
6 TXN_10822 USER_5052 3.79 POS 07-11-2023 01:18 86834.18 Tablet London Restaurants 0 0 2 298.35 2 Mastercard 168 3757.19 Password 0.0875 0 0
7 TXN_49498 USER_4660 7.08 ATM Withdrawal 25-02-2023 03:43 45826.27 Tablet London Restaurants 0 0 3 164.38 4 Discover 182 1764.66 Biometric 0.5326 0 1
8 TXN_4144 USER_1584 34.25 ATM Withdrawal 09-03-2023 22:51 94392.35 Tablet Tokyo Clothing 0 0 7 90.02 3 Visa 24 550.38 Biometric 0.1347 1 0
9 TXN_36958 USER_9498 16.24 POS 20-09-2023 17:27 91859.97 Mobile Mumbai Travel 0 0 6 474.42 1 Mastercard 124 720.91 PIN 0.3394 0 0
Transaction_ID User_ID Transaction_Amount Transaction_Type Timestamp Account_Balance Device_Type Location Merchant_Category IP_Address_Flag Previous_Fraudulent_Activity Daily_Transaction_Count Avg_Transaction_Amount_7d Failed_Transaction_Count_7d Card_Type Card_Age Transaction_Distance Authentication_Method Risk_Score Is_Weekend Fraud_Label
49990 TXN_47191 USER_1037 25.00 Online 06-03-2023 10:03 11434.89 Tablet Tokyo Travel 0 1 8 204.59 3 Amex 227 226.29 Password 0.0179 0 0
49991 TXN_21962 USER_2360 168.41 POS 06-09-2023 06:16 36626.82 Tablet Mumbai Travel 0 0 7 341.51 0 Mastercard 39 2862.22 OTP 0.1860 1 0
49992 TXN_37194 USER_5730 315.08 ATM Withdrawal 22-03-2023 06:48 98126.81 Tablet New York Restaurants 0 0 11 53.74 1 Mastercard 138 1531.42 OTP 0.5323 0 0
49993 TXN_16850 USER_4192 202.66 Bank Transfer 18-04-2023 09:22 98989.44 Laptop London Groceries 0 0 6 366.35 4 Visa 195 1939.25 Password 0.3458 1 1
49994 TXN_6265 USER_2098 109.62 Online 07-10-2023 13:37 57076.91 Mobile Tokyo Clothing 0 0 10 466.26 2 Visa 192 2631.85 Biometric 0.2922 1 0
49995 TXN_11284 USER_4796 45.05 Online 29-01-2023 18:38 76960.11 Mobile Tokyo Clothing 0 0 2 389.00 3 Amex 98 1537.54 PIN 0.1493 1 0
49996 TXN_44732 USER_1171 126.15 POS 09-05-2023 08:55 28791.75 Mobile Tokyo Clothing 0 0 13 434.95 4 Visa 93 2555.72 Biometric 0.3653 0 1
49997 TXN_38158 USER_2510 72.02 Online 30-01-2023 19:32 29916.41 Laptop Mumbai Clothing 0 1 1 369.15 2 Visa 114 4686.59 Biometric 0.5195 0 0
49998 TXN_860 USER_2248 64.89 Bank Transfer 09-03-2023 19:47 67895.67 Mobile Tokyo Electronics 0 0 13 242.29 4 Discover 72 4886.92 Biometric 0.7063 0 1
49999 TXN_15795 USER_6529 13.00 Bank Transfer 19-08-2023 23:57 7668.82 Tablet London Restaurants 0 0 5 273.78 1 Mastercard 154 1568.95 OTP 0.8938 0 1